X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learner
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913393204527104 |
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| author | Jiang, Haoyuan Li, Ziyue Wei, Hua Xiong, Xuantang Ruan, Jingqing Lu, Jiaming Mao, Hangyu Zhao, Rui |
| author_facet | Jiang, Haoyuan Li, Ziyue Wei, Hua Xiong, Xuantang Ruan, Jingqing Lu, Jiaming Mao, Hangyu Zhao, Rui |
| contents | The effectiveness of traffic light control has been significantly improved by current reinforcement learning-based approaches via better cooperation among multiple traffic lights. However, a persisting issue remains: how to obtain a multi-agent traffic signal control algorithm with remarkable transferability across diverse cities? In this paper, we propose a Transformer on Transformer (TonT) model for cross-city meta multi-agent traffic signal control, named as X-Light: We input the full Markov Decision Process trajectories, and the Lower Transformer aggregates the states, actions, rewards among the target intersection and its neighbors within a city, and the Upper Transformer learns the general decision trajectories across different cities. This dual-level approach bolsters the model's robust generalization and transferability. Notably, when directly transferring to unseen scenarios, ours surpasses all baseline methods with +7.91% on average, and even +16.3% in some cases, yielding the best results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_12090 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learner Jiang, Haoyuan Li, Ziyue Wei, Hua Xiong, Xuantang Ruan, Jingqing Lu, Jiaming Mao, Hangyu Zhao, Rui Artificial Intelligence The effectiveness of traffic light control has been significantly improved by current reinforcement learning-based approaches via better cooperation among multiple traffic lights. However, a persisting issue remains: how to obtain a multi-agent traffic signal control algorithm with remarkable transferability across diverse cities? In this paper, we propose a Transformer on Transformer (TonT) model for cross-city meta multi-agent traffic signal control, named as X-Light: We input the full Markov Decision Process trajectories, and the Lower Transformer aggregates the states, actions, rewards among the target intersection and its neighbors within a city, and the Upper Transformer learns the general decision trajectories across different cities. This dual-level approach bolsters the model's robust generalization and transferability. Notably, when directly transferring to unseen scenarios, ours surpasses all baseline methods with +7.91% on average, and even +16.3% in some cases, yielding the best results. |
| title | X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learner |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2404.12090 |